Intimate Partner Violence and Intimate Partner Homicide: Development of a Typology Based on Psychosocial Characteristics
Bibliographic record
Abstract
Intimate partner violence (IPV) remains an important and alarming global issue. Studies have put forth different profiles of perpetrators of IPV according to the severity of the violence and the presence of psychopathology. The objective of this study was to develop a typology of perpetrators of IPV and intimate partner homicide (IPH) according to their criminological, situational, and psychological characteristics, such as alexithymia. Alexithymia is when a person has difficulty identifying and describing emotions and in distinguishing feelings from bodily sensations of emotional arousal. Data were collected from 67 male perpetrators of IPV and/or homicide. Cluster analyses suggest four profiles: the homicial abandoned partner (19.4%), the generally angry/aggressive partner (23.9%), the controlling violent partner (34.3%), and the unstable dependent partner (22.4%). Comparative analyses show that the majority of the homicidal abandoned partners had committed IPH, had experienced the breakup of a relationship, and had a history of self-destructive behaviors; the generally angry/aggressive partners were perpetrators of IPV without homicide with a criminal history and who were alexithymic; the controlling violent partners had a criminal lifestyle and committed IPH; and the unstable dependent partners had committed IPV without homicide, were alexithymic, but had no criminal history. Establish a better understanding of the psychological issues within each profile of perpetrators of violence within the couple can help promote the prevention of IPV and can help devise interventions for these individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".